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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Geetha, R. Krishnamoorthy, N.V. Shakthi Murugan, K.H. Gnanaprakasam, C. Swarna, M. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Kuniyamuthur Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering AndhraPradesh Gudur524101 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Poonamallee Chennai600123 India

ISBN: (纸本)9798350347791

One of the most time-consuming and complex study subjects in the present day is trying to anticipate the amount of stress experienced by working professions. Although short-term stress might help us perform under pressure, chronic or severe stress compromises our well-being and causes disruptions in our daily life. Many stress-related health issues are avoidable if identified and treated early. A person's stress level may be determined by monitoring changes in a variety of biosignals, including temperature, electromagnetic, resistive, acoustical, visual and so on. There are a plethora of studies that may help you figure out what causes your stress and what kinds of things help you deal with it. Previous studies had a number of drawbacks, including more design complexity, more mistakes, worse efficiency, and a higher rate of misclassification. This research aims to apply an enhanced deep learning technique called Learning based Novel Stress Analyzer (LNSA) to forecast the level of stress of industry professionals in an effort to address these problems. Included in the methods proposed by this study are information preparation, extraction of features, classification and LNSA-based identification. Professionals' logical characteristic data is gathered via sensors and used as a dataset for processing the complete system. The sensors known as 3-Axis Accelerometer, Electrocardiogram, Pulse Sensor, Body Temperature and Respiration Sensor collect the defining data. Specifically, it eliminates redundant characteristics and completes incomplete data in the relevant dataset through a process called pre-processing. This paper provides the final classification findings, in which it show high accuracy in results and the technique of LNSA suggested in this paper accurately estimate the level of stress in a given situation. © 2023 IEEE.

关键词： Classification (of information)

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Yang, Jin Qiu, Peijie Zhang, Yichi Marcus, Daniel S. Sotiras, Aristeidis Mallinckrodt Institute of Radiology Washington University School of Medicine in St. Louis St. LouisMO63110 United States School of Data Science Fudan University Shanghai Shanghai China Institute for Informatics Data Science and Biostatistics Washington University School of Medicine in St. Louis St. LouisMO63110 United States

Hierarchical Vision Transformers (ViT) have achieved significant success in medical image segmentation due to their large receptive field and capabilities of leveraging long-range contextual information. Convolutional neural networks (CNNs) may also deliver a large receptive field by using large convolutional kernels. However, due to the employment of fixed-sized kernels, CNNs incorporated with large kernels remain limited in their ability to adaptively capture multi-scale features from organs with large variations in shape and size. They are also unable to utilize global contextual information efficiently. To address these limitations, we propose lightweight Dynamic Large Kernel (DLK) and Dynamic Feature Fusion (DFF) modules. The DLK employs multiple large kernels with varying kernel sizes and dilation rates to capture multi-scale features. Subsequently, a dynamic selection mechanism is utilized to adaptively highlight the most important channel and spatial features based on global information. The DFF is proposed to adaptively fuse multi-scale local feature maps based on their global information. We construct a DLK-Net for medical image segmentation by incorporating DLK and DFF into a hierarchical ViT architecture. Large convolutional kernels are incorporated into hierarchical ViT architectures to utilize their scaling behavior, but they are unable to sufficiently extract low-level features due to feature embedding in ViT architectures. To tackle this limitation, we propose a Salience layer to extract low-level features from images at their original dimensions without feature embedding. This Salience layer employs a Channel Mixer to effectively capture global representations. We incorporated DLK, DFF, and the Salience layer into a hierarchical ViT architecture to develop a novel architecture, termed D-Net. D-Net can effectively utilize a multi-scale large receptive field and adaptively harness global contextual information. To further demonstrate the superiority o

关键词： Image segmentation

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RoTipBot: Robotic Handling of Thin and Flexible Objects Using Rotatable Tactile Sensors

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IEEE Transactions on Robotics 2025年 41卷 3684-3702页

作者： Jiaqi Jiang Xuyang Zhang Daniel Fernandes Gomes Thanh-Toan Do Shan Luo Department of Engineering King’s College London London U.K. School of Aerospace Engineering Beijing Institute of Technology Beijing China Department of Data Science and AI Monash University Clayton Australia

This article introduces RoTipBot, a novel robotic system for handling thin, flexible objects. Different from previous works that are limited to singulating them using suction cups or soft grippers, RoTipBot can count multiple layers and then grasp them simultaneously in a single grasp closure. Specifically, we first develop a vision-based tactile sensor named RoTip that can rotate and sense contact information around its tip. Equipped with two RoTip sensors, RoTipBot rolls and feeds multiple layers of thin, flexible objects into the centre between its fingers, enabling effective grasping. Moreover, we design a tactile-based grasping strategy that uses RoTip’s sensing ability to ensure both fingers maintain secure contact with the object while accurately counting the number of fed objects. Extensive experiments demonstrate the efficacy of the RoTip sensor and the RoTipBot approach. The results show that RoTipBot not only achieves a higher success rate but also grasps and counts multiple layers simultaneously—capabilities not possible with previous methods. Furthermore, RoTipBot operates up to three times faster than state-of-the-art methods. The success of RoTipBot paves the way for future research in object manipulation using mobilized tactile sensors.

关键词： Sensors Robot sensing systems Grasping Robots Tactile sensors Grippers Payloads Feeds Visual perception Assistive robots

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A Hybrid Cloud Security System using Cryptography 2

A Hybrid Cloud Security System using Cryptography

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Sudhakar, G. Azath, H. Priya M, Pachhaiammal Alias Edwinprabhakar, P.B. Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computer Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Information security is a major problem in cloud computing systems as the information may be physically placed anywhere in any data center throughout the networks. Due to the nature of cloud computing, user authentication, information integrity, and confidentiality are all brought up as significant challenges. In order to ensure confidentiality, integrity, and authentications while storing and accessing information from and to the data center as well as in peer contacts;it is advised to adopt a better, innovative, secure security method. Cloud security is a rapidly expanding area of computer and networks securities. In the cloud platforms paradigm, external data centers are used. It supports a range of programming languages used in the process of deploying web applications. The Cloud is built on a managed containers architecture that integrates information services and robust ecosystems for the deployment and operation of modern apps. In cloud computing, data securities are a major challenge that is dealt with using cryptographic methods. One potential method for encrypting data is the Advanced Encryption Standard (AES). After developing a cloud platform, the AES algorithm for encryption and the data Encryption Standard (DES) method for decryption will be used in this work to secure data. The performance study suggests that DES and AES cryptography can be used to secure data. Calculations of data encryption and decryption delay also show that increased data delay periods are caused by bigger data volumes. © 2023 IEEE.

关键词： Cryptography

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DEEP NEURAL TANGENT KERNEL AND LAPLACE KERNEL HAVE THE SAME RKHS 9

DEEP NEURAL TANGENT KERNEL AND LAPLACE KERNEL HAVE THE SAME ...

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9th International Conference on Learning Representations, ICLR 2021

作者： Chen, Lin Xu, Sheng Simons Institute for the Theory of Computing University of California Berkeley United States Department of Statistics and Data Science Yale University United States

We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere Sd−1. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere Sd−1 and when it is defined on the entire Rd © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Laplace transforms

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Hierarchical Crowdsourcing for data Labeling with Heterogeneous Crowd

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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International Conference on data Engineering

作者： Haodi Zhang Wenxi Huang Zhenhan Su Junyang Chen Di Jiang Lixin Fan Chen Zhang Defu Lian Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University WeBank Institute of Financial Technology Shenzhen University Webank Co. Ltd. School of Data Science University of Science and Technology of China

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework 1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

引用

International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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